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TRACK 5

🤖 Python and Ship

From simple scripts to a product in production. This track connects Python to Claude Code: setup, automation, APIs, tests, deployment, and the ship mindset. By the end, you'll have a real project published.

Python setup Scripts FastAPI Tests Deploy PYTHON + CLAUDE from script to product Ship it!
6
Modules
36
Topics
~4.5h
Duration
Practical
Level
Path progress: 0% 0 of 36

Learning path map

Detailed content

5.1~40 min

🐍 Python + Claude Code

Python setup with pyenv, virtual environments, dependencies, and your first AI-assisted scripts.

What is:

Install and manage Python versions with pyenv or native python3.

Why learn:

Without the right version, nothing works. Claude Code needs a consistent Python setup.

Key concepts:

pyenv install, pyenv local, python3 --version, PATH.

What is:

Virtual environments isolate dependencies by project.

Why learn:

Prevents conflicts and ensures reproducibility.

Key concepts:

python3 -m venv, uv venv, activate, .venv in .gitignore.

What is:

Install packages with pip or uv (a fast alternative to pip).

Why learn:

Claude Code generates requirements.txt; you need to know how to install dependencies and pin versions.

Key concepts:

pip install, pip freeze, requirements.txt, uv pip install.

What is:

Write simple Python scripts with help from Claude Code.

Why learn:

Automating repetitive tasks is the first step toward "shipping."

Key concepts:

argparse, pathlib, sys.argv, if __name__.

What is:

Use Claude Code to identify and fix bugs quickly.

Why learn:

Debugging takes 50% of development time; AI reduces that dramatically.

Key concepts:

Paste traceback, ask for a fix, breakpoints, pdb.

What is:

Add type hints with Claude’s help for more robust code.

Why learn:

Type hints improve autocomplete and reduce runtime bugs.

Key concepts:

str, int, list[str], Optional, TypedDict, mypy.

5.2~45 min

⚡ Script Automation

Web scraping, file processing, data transforms, and scheduling with cron.

What is:

Extract data from web pages with requests and BeautifulSoup.

Why learn:

Automation starts with data; scraping is the gateway.

Key concepts:

requests.get, BeautifulSoup, selectors, rate limiting.

What is:

Process hundreds of files with pathlib and glob.

Why learn:

Manual tasks that take hours become a 20-line script.

Key concepts:

pathlib.Path, glob, shutil, bulk rename.

What is:

Read, transform, and export data between CSV, JSON, and other formats.

Why learn:

Every data pipeline needs to convert formats.

Key concepts:

csv.DictReader, json.load/dump, basic pandas.

What is:

Schedule scripts to run automatically with cron/systemd.

Why learn:

Real automation = running without human intervention.

Key concepts:

crontab -e, cron syntax, logging, systemd timers.

What is:

Consume REST APIs from services like GitHub, Slack, etc.

Why learn:

Powerful scripts connect services to each other.

Key concepts:

requests, headers, auth tokens, pagination.

What is:

try/except, logging, retries, and graceful degradation.

Why learn:

A production script can't die silently.

Key concepts:

try/except, logging module, tenacity, exit codes.

5.3~50 min

🚀 FastAPI and Web Services

Build professional APIs with FastAPI, Pydantic, and automatic docs.

What is:

Create your first API with FastAPI in minutes.

Why learn:

FastAPI is the fastest Python framework for modern APIs.

Key concepts:

FastAPI(), @app.get, uvicorn, async.

What is:

Define data schemas with Pydantic BaseModel.

Why learn:

Automatic validation eliminates data bugs at the point of entry.

Key concepts:

BaseModel, Field, validators, response_model.

What is:

Implement the four basic operations of any API.

Why learn:

CRUD is the universal pattern; every API starts here.

Key concepts:

@app.post/get/put/delete, status codes, path params.

What is:

Add CORS, rate limiting, and basic authentication.

Why learn:

An API without auth is an invitation to disaster.

Key concepts:

CORSMiddleware, Depends, OAuth2, API keys.

What is:

FastAPI automatically generates Swagger UI and ReDoc.

Why learn:

Living documentation that makes debugging and onboarding easier.

Key concepts:

/docs, /redoc, OpenAPI schema, tags.

What is:

Run locally with uvicorn and deploy remotely with gunicorn.

Why learn:

An API that only runs locally isn’t a product.

Key concepts:

uvicorn, gunicorn, reverse proxy, HTTPS.

5.4~45 min

🧪 Automated Testing

pytest, fixtures, mocks, coverage, and TDD with Claude Code.

What is:

Write and run tests with pytest.

Why learn:

Tests are the safety net that allows for fast iteration.

Key concepts:

test_ prefix, assert, pytest -v, markers.

What is:

Fixtures set up state; conftest.py shares it across files.

Why learn:

Prevents duplication and keeps tests clean.

Key concepts:

@pytest.fixture, scope, conftest.py, parametrize.

What is:

Simulate external dependencies with unittest.mock.

Why learn:

Test without depending on a database, API, or filesystem.

Key concepts:

Mock, patch, MagicMock, side_effect.

What is:

Measure which lines of code are exercised by the tests.

Why learn:

You don't know what isn't tested until you measure it.

Key concepts:

pytest-cov, --cov=src, coverage html, branch coverage.

What is:

Write the test before the code, with Claude generating the implementation.

Why learn:

TDD + AI is a superpower: you define what, AI handles how.

Key concepts:

Red-green-refactor, test-first, specification by example.

What is:

Run tests automatically on every push/PR.

Why learn:

CI ensures no one (not even AI) breaks what works.

Key concepts:

GitHub Actions, pytest step, coverage badge, fail fast.

5.5~50 min

🐳 Deployment and Infrastructure

Docker, cloud deployment, environment management, and monitoring.

What is:

Package your Python app in a Docker container.

Why learn:

"Works on my machine" becomes "works everywhere".

Key concepts:

Dockerfile, docker build, docker run, multi-stage.

What is:

Orchestrate multiple containers (app + DB + redis) with a YAML file.

Why learn:

Real apps have more than one service.

Key concepts:

docker-compose.yml, services, volumes, networks.

What is:

Deploy with one command on platforms like Railway or Fly.io.

Why learn:

Cloud PaaS is the fastest way to get your app online.

Key concepts:

railway deploy, fly launch, Procfile, buildpacks.

What is:

Manage .env, secrets, and configs by environment (dev/staging/prod).

Why learn:

Leaked secrets = disaster; wrong config = downtime.

Key concepts:

.env, python-dotenv, pydantic-settings, 12-factor.

What is:

Structured logging, health checks, and basic alerts.

Why learn:

Without monitoring, you only discover problems when the user complains.

Key concepts:

structlog, /health endpoint, uptime monitoring.

What is:

Horizontal vs. vertical scaling, caching, and when to consider them.

Why learn:

Knowing when (and when NOT) to scale avoids overengineering.

Key concepts:

Replicas, load balancer, Redis cache, premature optimization.

5.6~35 min

🚢 Ship Your Product

MVP mindset, launch checklist, and the post-launch iteration cycle.

What is:

Build the minimum viable solution that delivers real value.

Why learn:

Perfectionism kills projects; MVPs launch projects.

Key concepts:

MVP, scope cutting, “good enough,” time-boxed.

What is:

Deciding what NOT to do is just as important as deciding what to do.

Why learn:

With AI, you can build quickly; deciding WHAT becomes the bottleneck.

Key concepts:

Must/should/could/won't, impact vs effort, backlog grooming.

What is:

Pre-release checklist: tests, docs, monitoring, rollback.

Why learn:

Launches without a checklist are launches with surprises.

Key concepts:

Pre-launch, smoke tests, rollback plan, feature flags.

What is:

Collect real feedback and turn it into improvements.

Why learn:

A product without feedback is guesswork.

Key concepts:

User feedback, analytics, bug reports, feature requests.

What is:

Short build-measure-learn cycles after launch.

Why learn:

The launch isn't the end; it's the beginning.

Key concepts:

Sprints, changelogs, semver, continuous deployment.

What is:

Paths after mastering Python + Claude Code: SaaS, open source, consulting.

Why learn:

Knowing the destination helps you choose the next step.

Key concepts:

SaaS, open source, freelance, portfolio, community.